Speech recognition experiments are performed on the TIMIT corpus [1]}, using the speechbrain [2]} framework. Mel filterbank features are extracted from the waveforms and fed into two convolutional layers, followed by recurrent layers of H=512 hidden units. After two additional linear layers and a final log-softmax activation, the network outputs log-probabilities of phoneme classes. The training is done using the connectionist temporal classification (CTC) loss [3]} and the Adadelta optimizer [4]} for 50 epochs. Batch-normalization [5]} is also used on feed-forward connections, as suggested in [6]}.
